Predictive analysis includes predictive modeling and machine learning. Predictive analysis may be useful to provide predictions of future events or outcomes based on past events, circumstances, known variables, and underlying assumptions. Such predictions may be helpful, for example, to forecast events such as weather, drive times, and operational life of equipment. Predictive analysis is also applied by businesses to predict inventory needs, operational life of equipment, and employee retention as well as track and improve efficiencies and monitor change.
Access and integration of data is the lifeline of a machine learning system. With respect to predictive analysis services, predictions should be based on current reliable data. However, developing and managing data pipelines to feed a machine learning system is costly and complicated by the scalability of new data. Therefore, a need exists for improved computer apparatuses with a novel architecture for increased efficiency of operation, data collection, analysis, and machine learning.